Overview

Brought to you by YData

Dataset statistics

Number of variables24
Number of observations76
Missing cells8
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.4 KiB
Average record size in memory193.7 B

Variable types

Categorical8
Numeric15
Text1

Alerts

Blocked shots is highly overall correlated with Faceoffs and 14 other fieldsHigh correlation
Date is highly overall correlated with Opponent and 1 other fieldsHigh correlation
Faceoffs is highly overall correlated with Blocked shots and 14 other fieldsHigh correlation
Faceoffs in DZ is highly overall correlated with Blocked shots and 12 other fieldsHigh correlation
Faceoffs in NZ is highly overall correlated with Blocked shots and 15 other fieldsHigh correlation
Faceoffs in OZ is highly overall correlated with Blocked shots and 13 other fieldsHigh correlation
Faceoffs won is highly overall correlated with Blocked shots and 15 other fieldsHigh correlation
Faceoffs won in DZ is highly overall correlated with Blocked shots and 14 other fieldsHigh correlation
Faceoffs won in DZ, % is highly overall correlated with TypeHigh correlation
Faceoffs won in NZ is highly overall correlated with Blocked shots and 14 other fieldsHigh correlation
Faceoffs won in NZ, % is highly overall correlated with Blocked shots and 9 other fieldsHigh correlation
Faceoffs won in OZ is highly overall correlated with Blocked shots and 11 other fieldsHigh correlation
Faceoffs won in OZ, % is highly overall correlated with Blocked shots and 5 other fieldsHigh correlation
Goals is highly overall correlated with Blocked shots and 13 other fieldsHigh correlation
Hits is highly overall correlated with Penalty timeHigh correlation
Opponent is highly overall correlated with Date and 1 other fieldsHigh correlation
Penalties is highly overall correlated with Blocked shots and 14 other fieldsHigh correlation
Penalties drawn is highly overall correlated with Blocked shots and 15 other fieldsHigh correlation
Penalty time is highly overall correlated with Faceoffs in DZ and 8 other fieldsHigh correlation
Score is highly overall correlated with Date and 1 other fieldsHigh correlation
Shots is highly overall correlated with Blocked shots and 15 other fieldsHigh correlation
Shots on goal is highly overall correlated with Blocked shots and 14 other fieldsHigh correlation
Type is highly overall correlated with Faceoffs and 9 other fieldsHigh correlation
Date has 4 (5.3%) missing values Missing
Score has 4 (5.3%) missing values Missing
Date is uniformly distributed Uniform
Type is uniformly distributed Uniform
Goals has 42 (55.3%) zeros Zeros
Penalties has 23 (30.3%) zeros Zeros
Penalties drawn has 30 (39.5%) zeros Zeros
Faceoffs won has 1 (1.3%) zeros Zeros
Hits has 48 (63.2%) zeros Zeros
Faceoffs in DZ has 10 (13.2%) zeros Zeros
Faceoffs won in DZ has 14 (18.4%) zeros Zeros
Faceoffs in NZ has 15 (19.7%) zeros Zeros
Faceoffs won in NZ has 24 (31.6%) zeros Zeros
Faceoffs in OZ has 13 (17.1%) zeros Zeros
Faceoffs won in OZ has 19 (25.0%) zeros Zeros
Shots has 10 (13.2%) zeros Zeros
Shots on goal has 14 (18.4%) zeros Zeros
Blocked shots has 21 (27.6%) zeros Zeros

Reproduction

Analysis started2025-02-20 17:24:48.531717
Analysis finished2025-02-20 17:25:25.620452
Duration37.09 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Date
Categorical

High correlation  Missing  Uniform 

Distinct17
Distinct (%)23.6%
Missing4
Missing (%)5.3%
Memory size740.0 B
08/02
23/01
 
4
24/01
 
4
31/01
 
4
07/01
 
4
Other values (12)
48 

Length

Max length8
Median length5
Mean length6.3333333
Min length5

Characters and Unicode

Total characters456
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row08/02
2nd row23/01
3rd row24/01
4th row31/01
5th row07/01

Common Values

ValueCountFrequency (%)
08/02 8
 
10.5%
23/01 4
 
5.3%
24/01 4
 
5.3%
31/01 4
 
5.3%
07/01 4
 
5.3%
01/11/24 4
 
5.3%
14/02 4
 
5.3%
23/11/24 4
 
5.3%
24/11/24 4
 
5.3%
17/11/24 4
 
5.3%
Other values (7) 28
36.8%

Length

2025-02-20T12:25:25.861672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
08/02 8
 
11.1%
23/01 4
 
5.6%
24/01 4
 
5.6%
31/01 4
 
5.6%
07/01 4
 
5.6%
01/11/24 4
 
5.6%
14/02 4
 
5.6%
23/11/24 4
 
5.6%
24/11/24 4
 
5.6%
17/11/24 4
 
5.6%
Other values (7) 28
38.9%

Most occurring characters

ValueCountFrequency (%)
1 116
25.4%
/ 104
22.8%
0 76
16.7%
2 68
14.9%
4 44
 
9.6%
3 16
 
3.5%
8 16
 
3.5%
7 12
 
2.6%
9 4
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 456
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 116
25.4%
/ 104
22.8%
0 76
16.7%
2 68
14.9%
4 44
 
9.6%
3 16
 
3.5%
8 16
 
3.5%
7 12
 
2.6%
9 4
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 456
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 116
25.4%
/ 104
22.8%
0 76
16.7%
2 68
14.9%
4 44
 
9.6%
3 16
 
3.5%
8 16
 
3.5%
7 12
 
2.6%
9 4
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 456
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 116
25.4%
/ 104
22.8%
0 76
16.7%
2 68
14.9%
4 44
 
9.6%
3 16
 
3.5%
8 16
 
3.5%
7 12
 
2.6%
9 4
 
0.9%

Opponent
Categorical

High correlation 

Distinct14
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Memory size740.0 B
vs Neumann Knights
@ Wilkes Colonels
@ Arcadia University Knights
@ Lebanon Valley Flying Dutchmen
@ Massachusetts College of Liberal Arts
Other values (9)
36 

Length

Max length39
Median length32
Mean length24.052632
Min length16

Characters and Unicode

Total characters1828
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowvs Stevenson Mustangs
2nd rowvs Neumann Knights
3rd rowvs Neumann Knights
4th rowvs Lebanon Valley Flying Dutchmen
5th rowvs King's Monarchs

Common Values

ValueCountFrequency (%)
vs Neumann Knights 8
10.5%
@ Wilkes Colonels 8
10.5%
@ Arcadia University Knights 8
10.5%
@ Lebanon Valley Flying Dutchmen 8
10.5%
@ Massachusetts College of Liberal Arts 8
10.5%
vs Stevenson Mustangs 4
 
5.3%
vs Arcadia University Knights 4
 
5.3%
vs Hilbert College 4
 
5.3%
vs King's Monarchs 4
 
5.3%
vs Lebanon Valley Flying Dutchmen 4
 
5.3%
Other values (4) 16
21.1%

Length

2025-02-20T12:25:26.090786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
44
 
15.3%
vs 28
 
9.7%
knights 20
 
6.9%
valley 12
 
4.2%
arcadia 12
 
4.2%
lebanon 12
 
4.2%
university 12
 
4.2%
flying 12
 
4.2%
dutchmen 12
 
4.2%
college 12
 
4.2%
Other values (17) 112
38.9%

Most occurring characters

ValueCountFrequency (%)
212
 
11.6%
s 160
 
8.8%
e 156
 
8.5%
n 148
 
8.1%
a 108
 
5.9%
l 104
 
5.7%
i 100
 
5.5%
t 88
 
4.8%
o 68
 
3.7%
g 68
 
3.7%
Other values (27) 616
33.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1828
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
212
 
11.6%
s 160
 
8.8%
e 156
 
8.5%
n 148
 
8.1%
a 108
 
5.9%
l 104
 
5.7%
i 100
 
5.5%
t 88
 
4.8%
o 68
 
3.7%
g 68
 
3.7%
Other values (27) 616
33.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1828
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
212
 
11.6%
s 160
 
8.8%
e 156
 
8.5%
n 148
 
8.1%
a 108
 
5.9%
l 104
 
5.7%
i 100
 
5.5%
t 88
 
4.8%
o 68
 
3.7%
g 68
 
3.7%
Other values (27) 616
33.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1828
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
212
 
11.6%
s 160
 
8.8%
e 156
 
8.5%
n 148
 
8.1%
a 108
 
5.9%
l 104
 
5.7%
i 100
 
5.5%
t 88
 
4.8%
o 68
 
3.7%
g 68
 
3.7%
Other values (27) 616
33.7%

Score
Categorical

High correlation  Missing 

Distinct13
Distinct (%)18.1%
Missing4
Missing (%)5.3%
Memory size740.0 B
1:4
16 
1:2
0:4
3:4
1:0
Other values (8)
32 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters216
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4:7
2nd row1:0
3rd row3:4
4th row2:6
5th row1:4

Common Values

ValueCountFrequency (%)
1:4 16
21.1%
1:2 8
10.5%
0:4 8
10.5%
3:4 4
 
5.3%
1:0 4
 
5.3%
4:7 4
 
5.3%
3:2 4
 
5.3%
2:6 4
 
5.3%
1:7 4
 
5.3%
0:5 4
 
5.3%
Other values (3) 12
15.8%

Length

2025-02-20T12:25:26.286970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1:4 16
22.2%
1:2 8
11.1%
0:4 8
11.1%
3:4 4
 
5.6%
1:0 4
 
5.6%
4:7 4
 
5.6%
3:2 4
 
5.6%
2:6 4
 
5.6%
1:7 4
 
5.6%
0:5 4
 
5.6%
Other values (3) 12
16.7%

Most occurring characters

ValueCountFrequency (%)
: 72
33.3%
1 36
16.7%
4 32
14.8%
2 20
 
9.3%
0 20
 
9.3%
3 20
 
9.3%
7 8
 
3.7%
6 4
 
1.9%
5 4
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 216
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
: 72
33.3%
1 36
16.7%
4 32
14.8%
2 20
 
9.3%
0 20
 
9.3%
3 20
 
9.3%
7 8
 
3.7%
6 4
 
1.9%
5 4
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 216
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
: 72
33.3%
1 36
16.7%
4 32
14.8%
2 20
 
9.3%
0 20
 
9.3%
3 20
 
9.3%
7 8
 
3.7%
6 4
 
1.9%
5 4
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 216
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
: 72
33.3%
1 36
16.7%
4 32
14.8%
2 20
 
9.3%
0 20
 
9.3%
3 20
 
9.3%
7 8
 
3.7%
6 4
 
1.9%
5 4
 
1.9%

Goals
Real number (ℝ)

High correlation  Zeros 

Distinct9
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.63907895
Minimum0
Maximum4
Zeros42
Zeros (%)55.3%
Negative0
Negative (%)0.0%
Memory size740.0 B
2025-02-20T12:25:26.433322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum4
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.92198652
Coefficient of variation (CV)1.4426802
Kurtosis2.4076674
Mean0.63907895
Median Absolute Deviation (MAD)0
Skewness1.6288239
Sum48.57
Variance0.85005914
MonotonicityNot monotonic
2025-02-20T12:25:26.616383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 42
55.3%
1 20
26.3%
2 5
 
6.6%
3 4
 
5.3%
4 1
 
1.3%
1.28 1
 
1.3%
1.06 1
 
1.3%
0.17 1
 
1.3%
0.06 1
 
1.3%
ValueCountFrequency (%)
0 42
55.3%
0.06 1
 
1.3%
0.17 1
 
1.3%
1 20
26.3%
1.06 1
 
1.3%
1.28 1
 
1.3%
2 5
 
6.6%
3 4
 
5.3%
4 1
 
1.3%
ValueCountFrequency (%)
4 1
 
1.3%
3 4
 
5.3%
2 5
 
6.6%
1.28 1
 
1.3%
1.06 1
 
1.3%
1 20
26.3%
0.17 1
 
1.3%
0.06 1
 
1.3%
0 42
55.3%

Penalties
Real number (ℝ)

High correlation  Zeros 

Distinct12
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1528947
Minimum0
Maximum7
Zeros23
Zeros (%)30.3%
Negative0
Negative (%)0.0%
Memory size740.0 B
2025-02-20T12:25:26.774496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.5
Q34
95-th percentile5.25
Maximum7
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0279854
Coefficient of variation (CV)0.94198076
Kurtosis-1.0670121
Mean2.1528947
Median Absolute Deviation (MAD)1.5
Skewness0.4673898
Sum163.62
Variance4.1127248
MonotonicityNot monotonic
2025-02-20T12:25:26.924978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 23
30.3%
1 13
17.1%
3 11
14.5%
4 9
 
11.8%
5 8
 
10.5%
2 4
 
5.3%
6 3
 
3.9%
7 1
 
1.3%
3.6 1
 
1.3%
4.3 1
 
1.3%
Other values (2) 2
 
2.6%
ValueCountFrequency (%)
0 23
30.3%
0.33 1
 
1.3%
0.39 1
 
1.3%
1 13
17.1%
2 4
 
5.3%
3 11
14.5%
3.6 1
 
1.3%
4 9
 
11.8%
4.3 1
 
1.3%
5 8
 
10.5%
ValueCountFrequency (%)
7 1
 
1.3%
6 3
 
3.9%
5 8
10.5%
4.3 1
 
1.3%
4 9
11.8%
3.6 1
 
1.3%
3 11
14.5%
2 4
 
5.3%
1 13
17.1%
0.39 1
 
1.3%

Penalties drawn
Real number (ℝ)

High correlation  Zeros 

Distinct12
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6952632
Minimum0
Maximum7
Zeros30
Zeros (%)39.5%
Negative0
Negative (%)0.0%
Memory size740.0 B
2025-02-20T12:25:27.115305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.5
Q33
95-th percentile4.25
Maximum7
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7694982
Coefficient of variation (CV)1.0437897
Kurtosis-0.27683589
Mean1.6952632
Median Absolute Deviation (MAD)1.5
Skewness0.71310258
Sum128.84
Variance3.1311239
MonotonicityNot monotonic
2025-02-20T12:25:27.255140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 30
39.5%
2 12
 
15.8%
3 11
 
14.5%
4 9
 
11.8%
1 6
 
7.9%
5 2
 
2.6%
7 1
 
1.3%
3.4 1
 
1.3%
3.1 1
 
1.3%
6 1
 
1.3%
Other values (2) 2
 
2.6%
ValueCountFrequency (%)
0 30
39.5%
0.06 1
 
1.3%
0.28 1
 
1.3%
1 6
 
7.9%
2 12
 
15.8%
3 11
 
14.5%
3.1 1
 
1.3%
3.4 1
 
1.3%
4 9
 
11.8%
5 2
 
2.6%
ValueCountFrequency (%)
7 1
 
1.3%
6 1
 
1.3%
5 2
 
2.6%
4 9
11.8%
3.4 1
 
1.3%
3.1 1
 
1.3%
3 11
14.5%
2 12
15.8%
1 6
7.9%
0.28 1
 
1.3%

Penalty time
Categorical

High correlation 

Distinct13
Distinct (%)17.1%
Missing0
Missing (%)0.0%
Memory size740.0 B
00:00
23 
02:00
13 
06:00
11 
08:00
10:00
Other values (8)
13 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters380
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)7.9%

Sample

1st row04:00
2nd row10:00
3rd row10:00
4th row12:00
5th row08:00

Common Values

ValueCountFrequency (%)
00:00 23
30.3%
02:00 13
17.1%
06:00 11
14.5%
08:00 9
 
11.8%
10:00 7
 
9.2%
04:00 4
 
5.3%
12:00 3
 
3.9%
37:00 1
 
1.3%
09:57 1
 
1.3%
08:23 1
 
1.3%
Other values (3) 3
 
3.9%

Length

2025-02-20T12:25:27.422523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00 23
30.3%
02:00 13
17.1%
06:00 11
14.5%
08:00 9
 
11.8%
10:00 7
 
9.2%
04:00 4
 
5.3%
12:00 3
 
3.9%
37:00 1
 
1.3%
09:57 1
 
1.3%
08:23 1
 
1.3%
Other values (3) 3
 
3.9%

Most occurring characters

ValueCountFrequency (%)
0 241
63.4%
: 76
 
20.0%
2 17
 
4.5%
6 11
 
2.9%
8 10
 
2.6%
1 10
 
2.6%
4 6
 
1.6%
3 4
 
1.1%
7 3
 
0.8%
9 1
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 380
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 241
63.4%
: 76
 
20.0%
2 17
 
4.5%
6 11
 
2.9%
8 10
 
2.6%
1 10
 
2.6%
4 6
 
1.6%
3 4
 
1.1%
7 3
 
0.8%
9 1
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 380
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 241
63.4%
: 76
 
20.0%
2 17
 
4.5%
6 11
 
2.9%
8 10
 
2.6%
1 10
 
2.6%
4 6
 
1.6%
3 4
 
1.1%
7 3
 
0.8%
9 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 380
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 241
63.4%
: 76
 
20.0%
2 17
 
4.5%
6 11
 
2.9%
8 10
 
2.6%
1 10
 
2.6%
4 6
 
1.6%
3 4
 
1.1%
7 3
 
0.8%
9 1
 
0.3%

Faceoffs
Real number (ℝ)

High correlation 

Distinct34
Distinct (%)44.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.842105
Minimum3
Maximum71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size740.0 B
2025-02-20T12:25:27.606369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4
Q17
median22.5
Q343.75
95-th percentile57.25
Maximum71
Range68
Interquartile range (IQR)36.75

Descriptive statistics

Standard deviation20.78336
Coefficient of variation (CV)0.77428205
Kurtosis-1.507089
Mean26.842105
Median Absolute Deviation (MAD)17
Skewness0.28674363
Sum2040
Variance431.94807
MonotonicityNot monotonic
2025-02-20T12:25:27.815085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
4 7
 
9.2%
8 6
 
7.9%
6 5
 
6.6%
7 5
 
6.6%
41 4
 
5.3%
51 3
 
3.9%
9 3
 
3.9%
54 3
 
3.9%
39 3
 
3.9%
37 3
 
3.9%
Other values (24) 34
44.7%
ValueCountFrequency (%)
3 2
 
2.6%
4 7
9.2%
5 2
 
2.6%
6 5
6.6%
7 5
6.6%
8 6
7.9%
9 3
3.9%
10 1
 
1.3%
11 3
3.9%
13 2
 
2.6%
ValueCountFrequency (%)
71 1
 
1.3%
63 1
 
1.3%
58 2
2.6%
57 1
 
1.3%
56 1
 
1.3%
55 2
2.6%
54 3
3.9%
53 1
 
1.3%
52 1
 
1.3%
51 3
3.9%

Faceoffs won
Real number (ℝ)

High correlation  Zeros 

Distinct29
Distinct (%)38.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.506579
Minimum0
Maximum42
Zeros1
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size740.0 B
2025-02-20T12:25:28.016475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median11
Q319
95-th percentile26.5
Maximum42
Range42
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.7099445
Coefficient of variation (CV)0.84386025
Kurtosis0.10290129
Mean11.506579
Median Absolute Deviation (MAD)8
Skewness0.78805172
Sum874.5
Variance94.283023
MonotonicityNot monotonic
2025-02-20T12:25:28.225239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2 10
 
13.2%
3 9
 
11.8%
4 5
 
6.6%
1 5
 
6.6%
17 5
 
6.6%
15 4
 
5.3%
20 4
 
5.3%
19 3
 
3.9%
12 3
 
3.9%
13 3
 
3.9%
Other values (19) 25
32.9%
ValueCountFrequency (%)
0 1
 
1.3%
1 5
6.6%
2 10
13.2%
2.7 1
 
1.3%
3 9
11.8%
3.8 1
 
1.3%
4 5
6.6%
5 2
 
2.6%
7 2
 
2.6%
8 1
 
1.3%
ValueCountFrequency (%)
42 1
 
1.3%
37 1
 
1.3%
31 1
 
1.3%
28 1
 
1.3%
26 1
 
1.3%
25 3
3.9%
23 1
 
1.3%
22 3
3.9%
21 1
 
1.3%
20 4
5.3%
Distinct38
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size740.0 B
2025-02-20T12:25:28.548224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3
Min length1

Characters and Unicode

Total characters228
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)26.3%

Sample

1st row51%
2nd row51%
3rd row43%
4th row35%
5th row49%
ValueCountFrequency (%)
43 6
 
7.9%
25 6
 
7.9%
33 5
 
6.6%
38 5
 
6.6%
50 4
 
5.3%
51 4
 
5.3%
36 3
 
3.9%
59 3
 
3.9%
35 2
 
2.6%
42 2
 
2.6%
Other values (28) 36
47.4%
2025-02-20T12:25:29.030703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
% 75
32.9%
3 31
13.6%
5 24
 
10.5%
4 21
 
9.2%
2 18
 
7.9%
0 13
 
5.7%
1 13
 
5.7%
6 13
 
5.7%
8 8
 
3.5%
7 7
 
3.1%
Other values (2) 5
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 228
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
% 75
32.9%
3 31
13.6%
5 24
 
10.5%
4 21
 
9.2%
2 18
 
7.9%
0 13
 
5.7%
1 13
 
5.7%
6 13
 
5.7%
8 8
 
3.5%
7 7
 
3.1%
Other values (2) 5
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 228
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
% 75
32.9%
3 31
13.6%
5 24
 
10.5%
4 21
 
9.2%
2 18
 
7.9%
0 13
 
5.7%
1 13
 
5.7%
6 13
 
5.7%
8 8
 
3.5%
7 7
 
3.1%
Other values (2) 5
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 228
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
% 75
32.9%
3 31
13.6%
5 24
 
10.5%
4 21
 
9.2%
2 18
 
7.9%
0 13
 
5.7%
1 13
 
5.7%
6 13
 
5.7%
8 8
 
3.5%
7 7
 
3.1%
Other values (2) 5
 
2.2%

Hits
Real number (ℝ)

High correlation  Zeros 

Distinct8
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.41671053
Minimum0
Maximum3
Zeros48
Zeros (%)63.2%
Negative0
Negative (%)0.0%
Memory size740.0 B
2025-02-20T12:25:29.171338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum3
Range3
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.65162645
Coefficient of variation (CV)1.5637389
Kurtosis2.7530565
Mean0.41671053
Median Absolute Deviation (MAD)0
Skewness1.6550807
Sum31.67
Variance0.42461704
MonotonicityNot monotonic
2025-02-20T12:25:29.312109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 48
63.2%
1 19
 
25.0%
2 4
 
5.3%
3 1
 
1.3%
0.83 1
 
1.3%
0.56 1
 
1.3%
0.17 1
 
1.3%
0.11 1
 
1.3%
ValueCountFrequency (%)
0 48
63.2%
0.11 1
 
1.3%
0.17 1
 
1.3%
0.56 1
 
1.3%
0.83 1
 
1.3%
1 19
 
25.0%
2 4
 
5.3%
3 1
 
1.3%
ValueCountFrequency (%)
3 1
 
1.3%
2 4
 
5.3%
1 19
 
25.0%
0.83 1
 
1.3%
0.56 1
 
1.3%
0.17 1
 
1.3%
0.11 1
 
1.3%
0 48
63.2%

Faceoffs in DZ
Real number (ℝ)

High correlation  Zeros 

Distinct31
Distinct (%)40.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.210526
Minimum0
Maximum54
Zeros10
Zeros (%)13.2%
Negative0
Negative (%)0.0%
Memory size740.0 B
2025-02-20T12:25:29.507948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median11.5
Q322.25
95-th percentile31.75
Maximum54
Range54
Interquartile range (IQR)19.25

Descriptive statistics

Standard deviation11.444144
Coefficient of variation (CV)0.86628975
Kurtosis0.83662442
Mean13.210526
Median Absolute Deviation (MAD)8.5
Skewness0.8963588
Sum1004
Variance130.96842
MonotonicityNot monotonic
2025-02-20T12:25:29.746210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 10
 
13.2%
1 6
 
7.9%
8 6
 
7.9%
3 5
 
6.6%
23 5
 
6.6%
16 4
 
5.3%
31 3
 
3.9%
24 3
 
3.9%
13 3
 
3.9%
7 3
 
3.9%
Other values (21) 28
36.8%
ValueCountFrequency (%)
0 10
13.2%
1 6
7.9%
2 1
 
1.3%
3 5
6.6%
4 1
 
1.3%
5 1
 
1.3%
7 3
 
3.9%
8 6
7.9%
9 2
 
2.6%
10 2
 
2.6%
ValueCountFrequency (%)
54 1
 
1.3%
39 1
 
1.3%
35 1
 
1.3%
34 1
 
1.3%
31 3
3.9%
30 1
 
1.3%
26 2
 
2.6%
25 1
 
1.3%
24 3
3.9%
23 5
6.6%

Faceoffs won in DZ
Real number (ℝ)

High correlation  Zeros 

Distinct18
Distinct (%)23.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9539474
Minimum0
Maximum23
Zeros14
Zeros (%)18.4%
Negative0
Negative (%)0.0%
Memory size740.0 B
2025-02-20T12:25:29.899915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q38
95-th percentile12.5
Maximum23
Range23
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.6684313
Coefficient of variation (CV)0.94236595
Kurtosis1.8637311
Mean4.9539474
Median Absolute Deviation (MAD)3
Skewness1.1881386
Sum376.5
Variance21.794251
MonotonicityNot monotonic
2025-02-20T12:25:30.044569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 14
18.4%
1 9
11.8%
6 8
10.5%
2 7
9.2%
5 5
 
6.6%
10 5
 
6.6%
3 5
 
6.6%
8 4
 
5.3%
11 4
 
5.3%
4 3
 
3.9%
Other values (8) 12
15.8%
ValueCountFrequency (%)
0 14
18.4%
1 9
11.8%
2 7
9.2%
2.7 1
 
1.3%
3 5
 
6.6%
3.8 1
 
1.3%
4 3
 
3.9%
5 5
 
6.6%
6 8
10.5%
7 2
 
2.6%
ValueCountFrequency (%)
23 1
 
1.3%
17 1
 
1.3%
14 2
 
2.6%
12 1
 
1.3%
11 4
5.3%
10 5
6.6%
9 3
 
3.9%
8 4
5.3%
7 2
 
2.6%
6 8
10.5%

Faceoffs won in DZ, %
Categorical

High correlation 

Distinct31
Distinct (%)40.8%
Missing0
Missing (%)0.0%
Memory size740.0 B
-
14 
100%
38%
 
4
25%
 
4
33%
 
4
Other values (26)
45 

Length

Max length4
Median length3
Mean length2.6973684
Min length1

Characters and Unicode

Total characters205
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)17.1%

Sample

1st row38%
2nd row24%
3rd row38%
4th row20%
5th row40%

Common Values

ValueCountFrequency (%)
- 14
18.4%
100% 5
 
6.6%
38% 4
 
5.3%
25% 4
 
5.3%
33% 4
 
5.3%
24% 4
 
5.3%
39% 4
 
5.3%
43% 3
 
3.9%
31% 3
 
3.9%
50% 2
 
2.6%
Other values (21) 29
38.2%

Length

2025-02-20T12:25:30.258147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
14
18.4%
100 5
 
6.6%
38 4
 
5.3%
25 4
 
5.3%
33 4
 
5.3%
24 4
 
5.3%
39 4
 
5.3%
43 3
 
3.9%
31 3
 
3.9%
50 2
 
2.6%
Other values (21) 29
38.2%

Most occurring characters

ValueCountFrequency (%)
% 62
30.2%
3 29
14.1%
4 21
 
10.2%
0 19
 
9.3%
2 16
 
7.8%
- 14
 
6.8%
5 13
 
6.3%
1 12
 
5.9%
8 7
 
3.4%
9 6
 
2.9%
Other values (2) 6
 
2.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 205
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
% 62
30.2%
3 29
14.1%
4 21
 
10.2%
0 19
 
9.3%
2 16
 
7.8%
- 14
 
6.8%
5 13
 
6.3%
1 12
 
5.9%
8 7
 
3.4%
9 6
 
2.9%
Other values (2) 6
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 205
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
% 62
30.2%
3 29
14.1%
4 21
 
10.2%
0 19
 
9.3%
2 16
 
7.8%
- 14
 
6.8%
5 13
 
6.3%
1 12
 
5.9%
8 7
 
3.4%
9 6
 
2.9%
Other values (2) 6
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 205
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
% 62
30.2%
3 29
14.1%
4 21
 
10.2%
0 19
 
9.3%
2 16
 
7.8%
- 14
 
6.8%
5 13
 
6.3%
1 12
 
5.9%
8 7
 
3.4%
9 6
 
2.9%
Other values (2) 6
 
2.9%

Faceoffs in NZ
Real number (ℝ)

High correlation  Zeros 

Distinct19
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5
Minimum0
Maximum54
Zeros15
Zeros (%)19.7%
Negative0
Negative (%)0.0%
Memory size740.0 B
2025-02-20T12:25:30.418478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6.5
Q312
95-th percentile17.25
Maximum54
Range54
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.8249646
Coefficient of variation (CV)1.1766619
Kurtosis10.697842
Mean7.5
Median Absolute Deviation (MAD)5.5
Skewness2.5638309
Sum570
Variance77.88
MonotonicityNot monotonic
2025-02-20T12:25:30.583711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 15
19.7%
1 14
18.4%
13 6
 
7.9%
15 6
 
7.9%
12 5
 
6.6%
2 4
 
5.3%
10 4
 
5.3%
3 3
 
3.9%
9 3
 
3.9%
11 3
 
3.9%
Other values (9) 13
17.1%
ValueCountFrequency (%)
0 15
19.7%
1 14
18.4%
2 4
 
5.3%
3 3
 
3.9%
6 2
 
2.6%
7 2
 
2.6%
8 3
 
3.9%
9 3
 
3.9%
10 4
 
5.3%
11 3
 
3.9%
ValueCountFrequency (%)
54 1
 
1.3%
39 1
 
1.3%
19 1
 
1.3%
18 1
 
1.3%
17 1
 
1.3%
15 6
7.9%
14 1
 
1.3%
13 6
7.9%
12 5
6.6%
11 3
3.9%

Faceoffs won in NZ
Real number (ℝ)

High correlation  Zeros 

Distinct15
Distinct (%)19.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.375
Minimum0
Maximum23
Zeros24
Zeros (%)31.6%
Negative0
Negative (%)0.0%
Memory size740.0 B
2025-02-20T12:25:30.729430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q36
95-th percentile9
Maximum23
Range23
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.1280221
Coefficient of variation (CV)1.2231177
Kurtosis6.708472
Mean3.375
Median Absolute Deviation (MAD)2
Skewness2.1181201
Sum256.5
Variance17.040567
MonotonicityNot monotonic
2025-02-20T12:25:30.896334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 24
31.6%
1 11
14.5%
6 7
 
9.2%
7 6
 
7.9%
2 6
 
7.9%
4 5
 
6.6%
9 4
 
5.3%
3 4
 
5.3%
5 3
 
3.9%
8 1
 
1.3%
Other values (5) 5
 
6.6%
ValueCountFrequency (%)
0 24
31.6%
1 11
14.5%
2 6
 
7.9%
2.7 1
 
1.3%
3 4
 
5.3%
3.8 1
 
1.3%
4 5
 
6.6%
5 3
 
3.9%
6 7
 
9.2%
7 6
 
7.9%
ValueCountFrequency (%)
23 1
 
1.3%
17 1
 
1.3%
12 1
 
1.3%
9 4
5.3%
8 1
 
1.3%
7 6
7.9%
6 7
9.2%
5 3
3.9%
4 5
6.6%
3.8 1
 
1.3%

Faceoffs won in NZ, %
Categorical

High correlation 

Distinct29
Distinct (%)38.2%
Missing0
Missing (%)0.0%
Memory size740.0 B
-
24 
100%
10 
33%
67%
60%
Other values (24)
30 

Length

Max length4
Median length3
Mean length2.5
Min length1

Characters and Unicode

Total characters190
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)25.0%

Sample

1st row53%
2nd row70%
3rd row54%
4th row60%
5th row58%

Common Values

ValueCountFrequency (%)
- 24
31.6%
100% 10
13.2%
33% 4
 
5.3%
67% 4
 
5.3%
60% 4
 
5.3%
46% 3
 
3.9%
58% 2
 
2.6%
40% 2
 
2.6%
43% 2
 
2.6%
55% 2
 
2.6%
Other values (19) 19
25.0%

Length

2025-02-20T12:25:31.062133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
24
31.6%
100 10
13.2%
33 4
 
5.3%
67 4
 
5.3%
60 4
 
5.3%
46 3
 
3.9%
58 2
 
2.6%
40 2
 
2.6%
43 2
 
2.6%
55 2
 
2.6%
Other values (19) 19
25.0%

Most occurring characters

ValueCountFrequency (%)
% 52
27.4%
0 29
15.3%
- 24
12.6%
3 17
 
8.9%
1 13
 
6.8%
6 13
 
6.8%
5 11
 
5.8%
4 10
 
5.3%
7 9
 
4.7%
2 6
 
3.2%
Other values (2) 6
 
3.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 190
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
% 52
27.4%
0 29
15.3%
- 24
12.6%
3 17
 
8.9%
1 13
 
6.8%
6 13
 
6.8%
5 11
 
5.8%
4 10
 
5.3%
7 9
 
4.7%
2 6
 
3.2%
Other values (2) 6
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 190
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
% 52
27.4%
0 29
15.3%
- 24
12.6%
3 17
 
8.9%
1 13
 
6.8%
6 13
 
6.8%
5 11
 
5.8%
4 10
 
5.3%
7 9
 
4.7%
2 6
 
3.2%
Other values (2) 6
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 190
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
% 52
27.4%
0 29
15.3%
- 24
12.6%
3 17
 
8.9%
1 13
 
6.8%
6 13
 
6.8%
5 11
 
5.8%
4 10
 
5.3%
7 9
 
4.7%
2 6
 
3.2%
Other values (2) 6
 
3.2%

Faceoffs in OZ
Real number (ℝ)

High correlation  Zeros 

Distinct26
Distinct (%)34.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.9736842
Minimum0
Maximum54
Zeros13
Zeros (%)17.1%
Negative0
Negative (%)0.0%
Memory size740.0 B
2025-02-20T12:25:31.218230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median6
Q313.25
95-th percentile26.5
Maximum54
Range54
Interquartile range (IQR)11.25

Descriptive statistics

Standard deviation9.7720331
Coefficient of variation (CV)1.0889656
Kurtosis5.8617805
Mean8.9736842
Median Absolute Deviation (MAD)5
Skewness2.0529072
Sum682
Variance95.492632
MonotonicityNot monotonic
2025-02-20T12:25:31.628181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 13
17.1%
4 9
11.8%
8 6
 
7.9%
1 5
 
6.6%
5 5
 
6.6%
9 4
 
5.3%
10 3
 
3.9%
14 3
 
3.9%
6 3
 
3.9%
20 3
 
3.9%
Other values (16) 22
28.9%
ValueCountFrequency (%)
0 13
17.1%
1 5
 
6.6%
2 2
 
2.6%
3 2
 
2.6%
4 9
11.8%
5 5
 
6.6%
6 3
 
3.9%
7 2
 
2.6%
8 6
7.9%
9 4
 
5.3%
ValueCountFrequency (%)
54 1
 
1.3%
39 1
 
1.3%
32 1
 
1.3%
28 1
 
1.3%
26 1
 
1.3%
24 1
 
1.3%
20 3
3.9%
19 2
2.6%
18 1
 
1.3%
17 1
 
1.3%

Faceoffs won in OZ
Real number (ℝ)

High correlation  Zeros 

Distinct18
Distinct (%)23.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4013158
Minimum0
Maximum23
Zeros19
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size740.0 B
2025-02-20T12:25:31.774753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.75
median3
Q36
95-th percentile16.25
Maximum23
Range23
Interquartile range (IQR)5.25

Descriptive statistics

Standard deviation4.9712371
Coefficient of variation (CV)1.1294888
Kurtosis2.9664463
Mean4.4013158
Median Absolute Deviation (MAD)3
Skewness1.7142177
Sum334.5
Variance24.713198
MonotonicityNot monotonic
2025-02-20T12:25:31.925428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 19
25.0%
3 11
14.5%
2 9
11.8%
4 6
 
7.9%
6 4
 
5.3%
1 4
 
5.3%
7 4
 
5.3%
5 3
 
3.9%
8 3
 
3.9%
10 3
 
3.9%
Other values (8) 10
13.2%
ValueCountFrequency (%)
0 19
25.0%
1 4
 
5.3%
2 9
11.8%
2.7 1
 
1.3%
3 11
14.5%
3.8 1
 
1.3%
4 6
 
7.9%
5 3
 
3.9%
6 4
 
5.3%
7 4
 
5.3%
ValueCountFrequency (%)
23 1
 
1.3%
19 1
 
1.3%
17 2
2.6%
16 1
 
1.3%
14 1
 
1.3%
11 2
2.6%
10 3
3.9%
8 3
3.9%
7 4
5.3%
6 4
5.3%

Faceoffs won in OZ, %
Categorical

High correlation 

Distinct27
Distinct (%)35.5%
Missing0
Missing (%)0.0%
Memory size740.0 B
-
19 
50%
12 
25%
60%
43%
Other values (22)
32 

Length

Max length4
Median length3
Mean length2.5263158
Min length1

Characters and Unicode

Total characters192
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)19.7%

Sample

1st row61%
2nd row61%
3rd row42%
4th row44%
5th row50%

Common Values

ValueCountFrequency (%)
- 19
25.0%
50% 12
15.8%
25% 5
 
6.6%
60% 4
 
5.3%
43% 4
 
5.3%
75% 4
 
5.3%
44% 3
 
3.9%
33% 2
 
2.6%
61% 2
 
2.6%
62% 2
 
2.6%
Other values (17) 19
25.0%

Length

2025-02-20T12:25:32.092173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
19
25.0%
50 12
15.8%
25 5
 
6.6%
60 4
 
5.3%
43 4
 
5.3%
75 4
 
5.3%
44 3
 
3.9%
33 2
 
2.6%
61 2
 
2.6%
62 2
 
2.6%
Other values (17) 19
25.0%

Most occurring characters

ValueCountFrequency (%)
% 57
29.7%
5 27
14.1%
0 23
12.0%
- 19
 
9.9%
3 14
 
7.3%
2 12
 
6.2%
4 12
 
6.2%
6 11
 
5.7%
7 8
 
4.2%
1 4
 
2.1%
Other values (2) 5
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 192
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
% 57
29.7%
5 27
14.1%
0 23
12.0%
- 19
 
9.9%
3 14
 
7.3%
2 12
 
6.2%
4 12
 
6.2%
6 11
 
5.7%
7 8
 
4.2%
1 4
 
2.1%
Other values (2) 5
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 192
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
% 57
29.7%
5 27
14.1%
0 23
12.0%
- 19
 
9.9%
3 14
 
7.3%
2 12
 
6.2%
4 12
 
6.2%
6 11
 
5.7%
7 8
 
4.2%
1 4
 
2.1%
Other values (2) 5
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 192
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
% 57
29.7%
5 27
14.1%
0 23
12.0%
- 19
 
9.9%
3 14
 
7.3%
2 12
 
6.2%
4 12
 
6.2%
6 11
 
5.7%
7 8
 
4.2%
1 4
 
2.1%
Other values (2) 5
 
2.6%

Shots
Real number (ℝ)

High correlation  Zeros 

Distinct40
Distinct (%)52.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.362632
Minimum0
Maximum82
Zeros10
Zeros (%)13.2%
Negative0
Negative (%)0.0%
Memory size740.0 B
2025-02-20T12:25:32.294472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median15
Q334
95-th percentile59
Maximum82
Range82
Interquartile range (IQR)32

Descriptive statistics

Standard deviation20.206573
Coefficient of variation (CV)0.99233603
Kurtosis0.55909264
Mean20.362632
Median Absolute Deviation (MAD)14
Skewness1.0369124
Sum1547.56
Variance408.30559
MonotonicityNot monotonic
2025-02-20T12:25:32.509505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0 10
 
13.2%
1 6
 
7.9%
46 3
 
3.9%
41 3
 
3.9%
11 3
 
3.9%
26 3
 
3.9%
15 3
 
3.9%
6 3
 
3.9%
2 3
 
3.9%
7 3
 
3.9%
Other values (30) 36
47.4%
ValueCountFrequency (%)
0 10
13.2%
0.56 1
 
1.3%
1 6
7.9%
2 3
 
3.9%
3 2
 
2.6%
4 1
 
1.3%
6 3
 
3.9%
7 3
 
3.9%
8 1
 
1.3%
9 1
 
1.3%
ValueCountFrequency (%)
82 1
 
1.3%
75 1
 
1.3%
71 1
 
1.3%
62 1
 
1.3%
58 1
 
1.3%
52 1
 
1.3%
46 3
3.9%
45 1
 
1.3%
44 1
 
1.3%
41 3
3.9%

Shots on goal
Real number (ℝ)

High correlation  Zeros 

Distinct33
Distinct (%)43.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.388026
Minimum0
Maximum51
Zeros14
Zeros (%)18.4%
Negative0
Negative (%)0.0%
Memory size740.0 B
2025-02-20T12:25:32.754463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q316
95-th percentile29.5
Maximum51
Range51
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.147926
Coefficient of variation (CV)1.0731515
Kurtosis2.8114502
Mean10.388026
Median Absolute Deviation (MAD)7
Skewness1.5256312
Sum789.49
Variance124.27626
MonotonicityNot monotonic
2025-02-20T12:25:32.924052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 14
18.4%
1 6
 
7.9%
8 5
 
6.6%
16 4
 
5.3%
4 3
 
3.9%
2 3
 
3.9%
5 3
 
3.9%
23 3
 
3.9%
10 3
 
3.9%
3 3
 
3.9%
Other values (23) 29
38.2%
ValueCountFrequency (%)
0 14
18.4%
0.39 1
 
1.3%
1 6
7.9%
2 3
 
3.9%
3 3
 
3.9%
4 3
 
3.9%
4.1 1
 
1.3%
5 3
 
3.9%
6 1
 
1.3%
7 2
 
2.6%
ValueCountFrequency (%)
51 1
 
1.3%
50 1
 
1.3%
34 1
 
1.3%
31 1
 
1.3%
29 1
 
1.3%
26 1
 
1.3%
25 2
2.6%
24 1
 
1.3%
23 3
3.9%
21 1
 
1.3%

Blocked shots
Real number (ℝ)

High correlation  Zeros 

Distinct24
Distinct (%)31.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7863158
Minimum0
Maximum29
Zeros21
Zeros (%)27.6%
Negative0
Negative (%)0.0%
Memory size740.0 B
2025-02-20T12:25:33.078377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q310
95-th percentile19.5
Maximum29
Range29
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.5561202
Coefficient of variation (CV)1.1330388
Kurtosis1.7159117
Mean5.7863158
Median Absolute Deviation (MAD)4
Skewness1.3596896
Sum439.76
Variance42.982712
MonotonicityNot monotonic
2025-02-20T12:25:33.242214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 21
27.6%
1 7
 
9.2%
4 6
 
7.9%
12 5
 
6.6%
2 4
 
5.3%
9 4
 
5.3%
6 4
 
5.3%
10 4
 
5.3%
3 3
 
3.9%
7 3
 
3.9%
Other values (14) 15
19.7%
ValueCountFrequency (%)
0 21
27.6%
0.06 1
 
1.3%
1 7
 
9.2%
2 4
 
5.3%
2.7 1
 
1.3%
3 3
 
3.9%
4 6
 
7.9%
5 1
 
1.3%
6 4
 
5.3%
7 3
 
3.9%
ValueCountFrequency (%)
29 1
 
1.3%
24 1
 
1.3%
22 1
 
1.3%
21 1
 
1.3%
19 1
 
1.3%
16 1
 
1.3%
15 1
 
1.3%
14 1
 
1.3%
13 1
 
1.3%
12 5
6.6%

Type
Categorical

High correlation  Uniform 

Distinct4
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size740.0 B
GamesTotal
19 
EV
19 
PP
19 
PK
19 

Length

Max length10
Median length2
Mean length4
Min length2

Characters and Unicode

Total characters304
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGamesTotal
2nd rowGamesTotal
3rd rowGamesTotal
4th rowGamesTotal
5th rowGamesTotal

Common Values

ValueCountFrequency (%)
GamesTotal 19
25.0%
EV 19
25.0%
PP 19
25.0%
PK 19
25.0%

Length

2025-02-20T12:25:33.450109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T12:25:33.615788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
gamestotal 19
25.0%
ev 19
25.0%
pp 19
25.0%
pk 19
25.0%

Most occurring characters

ValueCountFrequency (%)
P 57
18.8%
a 38
12.5%
G 19
 
6.2%
e 19
 
6.2%
m 19
 
6.2%
s 19
 
6.2%
T 19
 
6.2%
t 19
 
6.2%
o 19
 
6.2%
l 19
 
6.2%
Other values (3) 57
18.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 304
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 57
18.8%
a 38
12.5%
G 19
 
6.2%
e 19
 
6.2%
m 19
 
6.2%
s 19
 
6.2%
T 19
 
6.2%
t 19
 
6.2%
o 19
 
6.2%
l 19
 
6.2%
Other values (3) 57
18.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 304
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 57
18.8%
a 38
12.5%
G 19
 
6.2%
e 19
 
6.2%
m 19
 
6.2%
s 19
 
6.2%
T 19
 
6.2%
t 19
 
6.2%
o 19
 
6.2%
l 19
 
6.2%
Other values (3) 57
18.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 304
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 57
18.8%
a 38
12.5%
G 19
 
6.2%
e 19
 
6.2%
m 19
 
6.2%
s 19
 
6.2%
T 19
 
6.2%
t 19
 
6.2%
o 19
 
6.2%
l 19
 
6.2%
Other values (3) 57
18.8%

Interactions

2025-02-20T12:25:22.303239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:24:50.890051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:24:53.609232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:24:55.797611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:24:58.003330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:00.583677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:02.706827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:05.229967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:07.299572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:09.471838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:11.828865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:13.963666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:16.114355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:18.281287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:20.188979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:22.462488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:24:51.202069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:24:53.753945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:24:55.951277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:24:58.175025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:00.722975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:02.862839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:05.395732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:07.469723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:09.618427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:12.049594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:14.103117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:16.272420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:18.408389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:20.365654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:22.605155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:24:51.350805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:24:53.894289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:24:56.093373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:24:58.563857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:00.847786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:02.998704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:05.535782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:07.617500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:09.761295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:12.196042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:14.238267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:16.398666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:18.532393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:20.505012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:22.765650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:24:51.550037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:24:54.032804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:24:56.220312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:24:58.771267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:00.986121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:03.173311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:05.669209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:07.749773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:09.902877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:12.344365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:14.363735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:16.525345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:18.654790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:20.637733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:22.900808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:24:51.731482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:24:54.190586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:24:56.369321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:24:58.924321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:01.164840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:03.382837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:05.795919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:07.884048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:10.050448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:12.502256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:14.508725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:16.660976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:18.808220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:20.775736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:23.034605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:24:51.905210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-20T12:24:59.220173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-20T12:25:06.081915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-20T12:25:10.392714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-20T12:25:19.052772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:21.074937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:23.307683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-20T12:25:06.199880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:08.292556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:10.544165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:12.875597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:14.971918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:17.041650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:19.162736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:21.220647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:23.486612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:24:52.396530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:24:54.764102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:24:57.025785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:24:59.509967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:01.689582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:03.958012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:06.348391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:08.412256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:10.675632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:12.994396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:15.094930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:17.160096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:19.281744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-20T12:25:11.077991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-20T12:25:17.532975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:19.662693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:21.766940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-20T12:25:07.149536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:09.335031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:11.682036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:13.823621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:15.961961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:17.948909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:20.053808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T12:25:22.165056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-02-20T12:25:33.835571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Blocked shotsDateFaceoffsFaceoffs in DZFaceoffs in NZFaceoffs in OZFaceoffs wonFaceoffs won in DZFaceoffs won in DZ, %Faceoffs won in NZFaceoffs won in NZ, %Faceoffs won in OZFaceoffs won in OZ, %GoalsHitsOpponentPenaltiesPenalties drawnPenalty timeScoreShotsShots on goalType
Blocked shots1.0000.1970.7240.5320.6880.9060.8090.5540.3490.6760.6200.8870.5490.6980.3410.0650.6880.7130.2540.2110.9450.8690.486
Date0.1971.0000.0000.0000.0000.1590.0000.0000.0000.0760.1320.1470.2360.1910.1760.9120.0000.2160.0000.9260.0000.0950.000
Faceoffs0.7240.0001.0000.8920.8080.7560.8870.8670.4560.7310.5080.7030.3600.6190.3950.0000.8330.8280.3800.0000.7840.7970.723
Faceoffs in DZ0.5320.0000.8921.0000.7530.5500.7370.9340.3590.6660.4430.4730.2220.4930.3570.0000.7950.7720.5850.0000.5980.6280.654
Faceoffs in NZ0.6880.0000.8080.7531.0000.7330.8690.7800.3310.9150.6110.7150.2300.7040.3750.1510.7980.8360.7380.0000.7730.7920.550
Faceoffs in OZ0.9060.1590.7560.5500.7331.0000.8220.5830.3750.7050.4990.9510.4050.6840.3880.2450.6880.7520.5630.2330.9200.8950.379
Faceoffs won0.8090.0000.8870.7370.8690.8221.0000.8060.4410.8520.5280.8460.5240.7190.3910.0000.8060.8670.2270.0000.8730.8840.581
Faceoffs won in DZ0.5540.0000.8670.9340.7800.5830.8061.0000.3500.6800.5590.5350.1400.5460.4210.0600.7640.7950.5710.0000.6160.6480.481
Faceoffs won in DZ, %0.3490.0000.4560.3590.3310.3750.4410.3501.0000.4240.4790.3930.2730.1650.0000.0000.1820.4670.2660.0000.3990.4100.552
Faceoffs won in NZ0.6760.0760.7310.6660.9150.7050.8520.6800.4241.0000.6450.6990.3360.7000.3190.2470.7660.7830.6710.2010.7510.7550.440
Faceoffs won in NZ, %0.6200.1320.5080.4430.6110.4990.5280.5590.4790.6451.0000.4550.4720.6070.3560.1860.4600.5990.4900.2180.5160.5850.444
Faceoffs won in OZ0.8870.1470.7030.4730.7150.9510.8460.5350.3930.6990.4551.0000.4640.7040.3340.1840.6530.7430.4420.1550.9040.8870.354
Faceoffs won in OZ, %0.5490.2360.3600.2220.2300.4050.5240.1400.2730.3360.4720.4641.0000.3930.3940.1670.2110.5730.3020.2290.5070.5450.514
Goals0.6980.1910.6190.4930.7040.6840.7190.5460.1650.7000.6070.7040.3931.0000.1910.3010.5870.5950.5270.3830.7560.7410.318
Hits0.3410.1760.3950.3570.3750.3880.3910.4210.0000.3190.3560.3340.3940.1911.0000.1880.3890.4650.6320.1770.3540.3570.211
Opponent0.0650.9120.0000.0000.1510.2450.0000.0600.0000.2470.1860.1840.1670.3010.1881.0000.1730.1060.2240.8130.0000.2010.000
Penalties0.6880.0000.8330.7950.7980.6880.8060.7640.1820.7660.4600.6530.2110.5870.3890.1731.0000.8120.9700.1550.7640.7690.529
Penalties drawn0.7130.2160.8280.7720.8360.7520.8670.7950.4670.7830.5990.7430.5730.5950.4650.1060.8121.0000.3500.1960.7700.7950.563
Penalty time0.2540.0000.3800.5850.7380.5630.2270.5710.2660.6710.4900.4420.3020.5270.6320.2240.9700.3501.0000.1360.2660.3430.519
Score0.2110.9260.0000.0000.0000.2330.0000.0000.0000.2010.2180.1550.2290.3830.1770.8130.1550.1960.1361.0000.0000.2440.000
Shots0.9450.0000.7840.5980.7730.9200.8730.6160.3990.7510.5160.9040.5070.7560.3540.0000.7640.7700.2660.0001.0000.9680.526
Shots on goal0.8690.0950.7970.6280.7920.8950.8840.6480.4100.7550.5850.8870.5450.7410.3570.2010.7690.7950.3430.2440.9681.0000.493
Type0.4860.0000.7230.6540.5500.3790.5810.4810.5520.4400.4440.3540.5140.3180.2110.0000.5290.5630.5190.0000.5260.4931.000

Missing values

2025-02-20T12:25:24.769056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-20T12:25:25.149957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-02-20T12:25:25.485590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

DateOpponentScoreGoalsPenaltiesPenalties drawnPenalty timeFaceoffsFaceoffs wonFaceoffs won, %HitsFaceoffs in DZFaceoffs won in DZFaceoffs won in DZ, %Faceoffs in NZFaceoffs won in NZFaceoffs won in NZ, %Faceoffs in OZFaceoffs won in OZFaceoffs won in OZ, %ShotsShots on goalBlocked shotsType
008/02vs Stevenson Mustangs4:74.02.04.004:005126.051%1.0166.038%179.053%1811.061%46.031.07.0GamesTotal
123/01vs Neumann Knights1:01.05.03.010:005528.051%0.0174.024%107.070%2817.061%82.034.029.0GamesTotal
224/01vs Neumann Knights3:43.05.03.010:005825.043%0.02610.038%137.054%198.042%58.025.024.0GamesTotal
331/01vs Lebanon Valley Flying Dutchmen2:62.06.02.012:005419.035%1.0306.020%159.060%94.044%41.019.010.0GamesTotal
407/01vs King's Monarchs1:41.04.03.008:004120.049%1.0156.040%127.058%147.050%34.011.012.0GamesTotal
501/11/24vs Hilbert College3:23.04.02.008:007142.059%0.02614.054%1912.063%2616.062%75.051.013.0GamesTotal
614/02vs Arcadia University Knights1:41.04.04.008:005222.042%0.03111.035%135.038%86.075%24.013.07.0GamesTotal
723/11/24@ Wilkes Colonels0:50.03.05.006:005620.036%3.03511.031%116.055%103.030%15.08.04.0GamesTotal
824/11/24@ Wilkes Colonels1:71.06.04.012:005725.044%0.03114.045%187.039%84.050%19.015.01.0GamesTotal
917/11/24@ Stevenson Mustangs1:41.03.04.006:004925.051%2.01911.058%144.029%1610.063%46.024.016.0GamesTotal
DateOpponentScoreGoalsPenaltiesPenalties drawnPenalty timeFaceoffsFaceoffs wonFaceoffs won, %HitsFaceoffs in DZFaceoffs won in DZFaceoffs won in DZ, %Faceoffs in NZFaceoffs won in NZFaceoffs won in NZ, %Faceoffs in OZFaceoffs won in OZFaceoffs won in OZ, %ShotsShots on goalBlocked shotsType
6617/11/24@ Stevenson Mustangs1:40.000.000.0000:0041.025%0.0041.025%00.0-00.0-1.001.000.00PK
6730/11/24@ Massachusetts College of Liberal Arts1:20.000.000.0000:0063.050%0.0031.033%22.0100%10.0-1.001.000.00PK
6801/12/24@ Massachusetts College of Liberal Arts1:20.000.000.0000:0073.043%0.0053.060%10.0-10.0-0.000.000.00PK
6917/01@ Lebanon Valley Flying Dutchmen0:30.000.000.0000:0061.017%0.0031.033%20.0-10.0-0.000.000.00PK
7018/01@ Lebanon Valley Flying Dutchmen1:40.000.000.0000:0082.025%0.0082.025%00.0-00.0-1.000.000.00PK
7101/02@ King's Monarchs2:30.001.000.0002:0082.025%1.0072.029%10.0-00.0-1.000.000.00PK
7208/11/24@ Arcadia University Knights1:30.001.000.0002:00142.014%0.00142.014%00.0-00.0-1.001.000.00PK
7309/11/24@ Arcadia University Knights0:40.001.000.0002:0092.022%0.0082.025%10.0-00.0-0.000.000.00PK
7408/02@ Alvernia Wolves0:40.001.001.0002:0093.033%1.0093.033%00.0-00.0-0.000.000.00PK
75NaNAverage per gameNaN0.060.330.0600:4082.732%0.1182.732%82.732%82.732%0.560.390.06PK